| Efficient MRF deformation model for non-rigid image matching A Shekhovtsov, I Kovtun, V Hlaváč Computer Vision and Image Understanding 112 (1), 91-99, 2008 | 215 | 2008 |
| End-to-end training of hybrid CNN-CRF models for stereo P Knobelreiter, C Reinbacher, A Shekhovtsov, T Pock Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 167 | 2017 |
| On partial optimality in multi-label MRFs P Kohli, A Shekhovtsov, C Rother, V Kolmogorov, P Torr Proceedings of the 25th international conference on Machine learning, 480-487, 2008 | 91 | 2008 |
| Scalable multi-view stereo M Jancosek, A Shekhovtsov, T Pajdla 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV …, 2009 | 50 | 2009 |
| A Distributed Mincut/Maxflow Algorithm Combining Path Augmentation and Push-Relabel ASV Hlavac International Journal of Computer Vision, 28, 2012 | 48* | 2012 |
| Automated integer programming based separation of arteries and veins from thoracic CT images C Payer, M Pienn, Z Bálint, A Shekhovtsov, E Talakic, E Nagy, ... Medical image analysis 34, 109-122, 2016 | 42 | 2016 |
| Partial optimality by pruning for MAP-inference with general graphical models P Swoboda, A Shekhovtsov, JH Kappes, C Schnoerr, B Savchynskyy IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 38 (7), 1370–1382, 2016 | 37 | 2016 |
| Curvature prior for MRF-based segmentation and shape inpainting A Shekhovtsov, P Kohli, C Rother Joint DAGM (German Association for Pattern Recognition) and OAGM Symposium …, 2012 | 37 | 2012 |
| Generalized differentiable RANSAC T Wei, Y Patel, A Shekhovtsov, J Matas, D Barath Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 33 | 2023 |
| Maximum persistency via iterative relaxed inference with graphical models A Shekhovtsov, P Swoboda, S Bogdan IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (7), 1668-1682, 2018 | 32 | 2018 |
| Feed-forward propagation in probabilistic neural networks with categorical and max layers A Shekhovtsov, B Flach International conference on learning representations, 2018 | 28* | 2018 |
| Belief propagation reloaded: Learning bp-layers for labeling problems P Knobelreiter, C Sormann, A Shekhovtsov, F Fraundorfer, T Pock Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 27 | 2020 |
| Complexity of discrete energy minimization problems M Li, A Shekhovtsov, D Huber Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 27 | 2016 |
| Solving dense image matching in real-time using discrete-continuous optimization A Shekhovtsov, C Reinbacher, G Graber, T Pock arXiv preprint arXiv:1601.06274, 2016 | 24 | 2016 |
| MPLP++: Fast, parallel dual block-coordinate ascent for dense graphical models S Tourani, A Shekhovtsov, C Rother, B Savchynskyy Proceedings of the European Conference on Computer Vision (ECCV), 251-267, 2018 | 22 | 2018 |
| Maximum persistency in energy minimization A Shekhovtsov Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014 | 22 | 2014 |
| VAE Approximation Error: ELBO and Exponential Families BF Alexander Shekhovtsov, Dmitrij Schlesinger International Conference on Learning Representations, 2022 | 20* | 2022 |
| Path sample-analytic gradient estimators for stochastic binary networks A Shekhovtsov, V Yanush, B Flach Advances in neural information processing systems 33, 12884-12894, 2020 | 19 | 2020 |
| Stochastic normalizations as bayesian learning A Shekhovtsov, B Flach Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth …, 2019 | 19 | 2019 |
| Joint M-best-diverse labelings as a parametric submodular minimization A Kirillov, A Shekhovtsov, C Rother, B Savchynskyy Advances in Neural Information Processing Systems 29, 2016 | 18 | 2016 |
Vaclav Hlavac (Václav Hlaváč)Professor of engineering cybernetics, Czech Technical university in Praguecvut.cz의 이메일 확인됨
